Data Driven Adaptive Predictive Control for Holonomic Constrained Under-Actuated Biped Robots

Fundamentally, control system designs are concerned with the flow of signals in the closed loop. In this paper, we are to present the control technique at the next level of abstraction in control system design. We construct a control using implicit function with support vector regression-based data-driven model for the biped, in the presence of parametric and functional dynamics uncertainties. Based on Lyapunov synthesis, we develop decoupled adaptive control based on the model predictive and the data-driven techniques and construct the control directly from online or offline data. The adaptive predictive control mechanisms use the advantage of data-driven technique combined with online parameters estimation strategy in order to achieve an efficient approximation. Simulation results are presented to verify the effectiveness of the proposed control.